## ---- eval=FALSE-------------------------------------------------------------- # install.packages("BiocManager") # BiocManager::install("MEB") ## ----------------------------------------------------------------------------- library(MEB) ## ----------------------------------------------------------------------------- data(sim_data_sp) sim_data_sp ## ----------------------------------------------------------------------------- data(real_data_sp) real_data_sp ## ----------------------------------------------------------------------------- data(sim_data_dsp) sim_data_dsp ## ----------------------------------------------------------------------------- data(real_data_dsp) real_data_dsp ## ---- message = FALSE, warning = FALSE---------------------------------------- library(SummarizedExperiment) ## ----------------------------------------------------------------------------- data(sim_data_sp) gamma <- seq(1e-06,5e-05,1e-06) sim_model_sp <- NIMEB(countsTable=assay(sim_data_sp), train_id=1:1000, gamma, nu = 0.01, reject_rate = 0.05, ds = FALSE) ## ----------------------------------------------------------------------------- data(real_data_sp) gamma <- seq(1e-06,5e-05,1e-06) real_model_sp <- NIMEB(countsTable=assay(real_data_sp), train_id=1:530, gamma, nu = 0.01, reject_rate = 0.1, ds = FALSE) ## ----------------------------------------------------------------------------- data(sim_data_dsp) gamma <- seq(1e-07,2e-05,1e-06) sim_model_dsp <- NIMEB(countsTable=assay(sim_data_dsp), train_id=1:1000, gamma, nu = 0.01, reject_rate = 0.1, ds = TRUE) ## ----------------------------------------------------------------------------- data(real_data_dsp) gamma <- seq(5e-08,5e-07,1e-08) real_model_dsp <- NIMEB(countsTable=assay(real_data_dsp), train_id=1:143, gamma, nu = 0.01, reject_rate = 0.1, ds = TRUE) ## ----------------------------------------------------------------------------- sim_model_sp_pred <- predict(sim_model_sp$model, assay(sim_data_sp)) summary(sim_model_sp_pred)